death_visuals

library(tidyverse)
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library(plotly)
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library(rgdal)
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library(raster)
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library(sf)
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library(spatialEco)
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library(viridis)
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knitr::opts_chunk$set(
  fig.width = 6,
  fig.asp = .6,
  out.width = "90%"
)
theme_set(theme_minimal() + theme(legend.position = "bottom"))
options(
  ggplot2.continuous.colour = "viridis",

  ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_d

covid_death_sdf = st_read("deathrate_map.shp") 
## Reading layer `deathrate_map' from data source 
##   `/Users/tanyabutt/Desktop/Data Science/p8105_finalproject/deathrate_map.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 178 features and 28 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49612 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  GCS_WGS84_DD


fig <- ggplotly(
  ggplot(covid_death_sdf) +
    geom_sf(aes(fill = cum_death)) +
    theme(legend.text = element_text(size = 6), 
        legend.position = 'right', axis.text.x = element_text(angle = 45)) +
    labs(
    title = "Average Death Rate per 100,000 people \n from March - September 2020 by ZCTA",
    x = "Latitude",
    y = "Longitude", fill = "Average Death Rate\n (deaths/100,000)")) 
  
fig